1 / 16

T he NOAA Hydrometeorology Testbed

Flooding on the Russian River, Guerneville. T he NOAA Hydrometeorology Testbed. A Briefing for University of Arizona Staff at NOAA/ESRL Boulder Colorado on 9 April, 2012, Marty Ralph. NOAA’s HMT Focus on extreme precipitation. NOAA’s Hydrometeorology Testbed (HMT)

galeno
Télécharger la présentation

T he NOAA Hydrometeorology Testbed

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Flooding on the Russian River, Guerneville The NOAAHydrometeorology Testbed A Briefing for University of Arizona Staff at NOAA/ESRL Boulder Colorado on 9 April, 2012, Marty Ralph

  2. NOAA’s HMT Focus on extreme precipitation • NOAA’s Hydrometeorology Testbed (HMT) • Connects researchers, forecasters and forecast users • Has been researching and developing prototypes on extreme precipitation in California since 2003 • Testing and applying results to the Pacific Northwest • Builds on earlier experiments from 1997-2002 • Lessons learned from HMT have been documented in over 50 formal peer-reviewed technical publications • http://hmt.noaa.gov/pubs/ http://hmt.noaa.gov

  3. A broad need emerged to better link the research community and forecasting in many topics • 1990’s – the weather community recognized there was a need for greater coordination between the weather research community and NWS • Both incremental improvements to existing forecast tools ANDbreakthrough advances that revolutionize forecast operations and skill are required. • NWS measures advances best by changes in forecast skill • Scientific community measures progress through evidence of innovation, e.g., peer review papers and inventions • TESTBEDS HAVE EMERGED AS A BRIDGE BETWEEN THESE DIFFERING COMMUNITIES

  4. Assessment of Extreme Quantitative Precipitation Forecasts (QPFs) and Development of Regional Extreme Event Thresholds Using Data from HMT-2006 and COOP Observers NWRFC CNRFC F. M. Ralph, E. Sukovich, D. Reynolds, M. Dettinger, S. Weagle, W. Clark, and P. J. Neiman Journal of Hydrometeorology (2010) The Forecasting Challenge GLA MAR UIL VER SKY SEA ABE SMP ENU CIN FRA OHA CUG AST LEE PDX DET SMI EUG CGR CNRFC CRL ILH SXT 4BK Mean Absolute error (in) Forecasting large precipitation amounts is difficult On average forecasts are 50% less than observations HON BKL BRR ORO FAR TKE BLU HYS GEO VNO PCH SMF CZC FOL Of the 20 dates with >3 inches of precipitation in 1 day, 18 were associated with ARs. RIO BND 41 West Coast sites were used TPK

  5. Weather-focused Testbeds have been created over the last 10 years The U.S. Weather Research Program has helped seed these and other testbeds since 2000 (see http://www.esrl.noaa.gov/research/uswrp/testbeds/ for links to several testbeds)

  6. NOAA Hydrometeorology Testbed (HMT) • HMT was established in 2003 to address scientific and practical challenges associated with extreme precipitation. • accelerate the development and prototyping of advanced hydrometeorological observations, models, and physical process understanding, and • foster infusion of these advances into forecasting operations of the NWS, and to support the broader user community's needs for 21st Century precipitation information. • HMT addresses these requirements through innovation, demonstration and infusion. • HMT is led by NOAA/ESRL's Physical Sciences Division with partners across NOAA, other agencies and universities.

  7. Major Stakeholders • NOAA (OAR Labs, USWRP, NWS/OHD, River Forecast Centers, WFOs, NCEP, NESDIS) • California DWR • California Energy Commission • Scripps Institution of Oceanography • Army Corps of Engineers • US Geological Survey • Sonoma County Water Agency • RENCI

  8. HMT Funding • Has ramped up from • $500 K in 2003 to • $5000 K in 2010 • Additional leveraging (supercomputing, CalWater, NASA…) • Roughly half of the current investment is in the form of NOAA Research core staff and facilities (mostly PSD) • Roughly half is from Project funds (3 main projects – USWRP, WRDA, DWR) • In FY11, The President requested an additional $7.7 M/year research base funds: • $5.0 M to strengthen and extend HMT’s core capabilities (i.e., long-term staff and equipment;, create HMT-SE; ensure continued efforts in the West), • $1.45 M to advance numerical weather modeling via THORPEX, • $1.2 M to link hydrologic stream forecasts to estuaries via CERIS

  9. New Directions for HMT • Establish HMT-Southeast • Intensive planning over the last 2 years • Rob Cifelli leads the implementation • Major partnership with NASA • Emerging climate applications • Key capability within NOAA Climate Service & links to NWS • Lessons from HMT will inform a HydroclimateTestbed • CalWater experiment w/California Energy Commission • Coordination w/NIDIS Pilot studies (CA, CO, & SE US)

  10. HMT's regional implementations started in California, have been extended to the Pacific Northwest, and are beginning in the Southeast. Rainfall - ”CATEGORY 3” events From >5000 COOP daily rainfall observations over roughly 50 years HMT-Southeast (2011 start) HMT-West (2003/4 start)

  11. “Water” is an emerging “NOAA Science Grand Challenge” • A formal NOAA report “Strengthening NOAA Science,” was developed from input across NOAA and was released by NOAA’s Administrator, Dr. Jane Lubchenkoin August 2010. The following was identified as a “Grand Challenge” • “Improve understanding of the water cycle at global to local scales to improve our ability to forecast weather, climate, water resources and ecosystem health.”

  12. HMT Organization • Five HMT Major Activity Areas (MAAs) • Quantitative Precipitation Estimation (QPE) • Quantitative Precipitation Forecasting (QPF) • Snow Information (SI) • Hydrologic Applications and Surface Processes (HASP) • Decision Support Tools (DST) • Four Infrastructure Support Functions: • Program coordination • Field Coordination • Science coordination • Transition coordination

  13. HMT Organization Program Director Marty Ralph Program Coordinator Richard Lataitis Science Coordinators Allen White / Rob Cifelli Field Coordinator Clark King Transition Coordinators David Reynolds / Tim Schneider Major Activity Areas Snow Information Hydrologic and Surface Processes Decision Support Tools Quantitative Precipitation Estimation Quantitative Precipitation Forecasting Stakeholder Groups Rob Cifelli Ken Howard Ellen Sukovich ZoltanToth Lynn Johnson Ed Clark HMT West/Northwest Allen White Allen White Art Henkel TBD Rob Cifelli Ken Howard HMT Southeast Pilot Rob Cifelli Transitions David Reynolds/Tim Schneider OAR NWS OAR NWS OAR NWS OAR NWS OAR NWS

  14. Testbeds Help Connect Research to NWS Forecast Operations • Testbeds can help, particularly with • Creating partnerships at the forecaster/researcher level • incremental improvements in existing forecast tools and • Developing and field testing high-risk/high-reward options that have the potential to create breakthrough advances • Testbeds have taken different forms depending on the forecast problem and state of the science/technology, e.g., • Hurricane prediction is very centralized, while severe weather warnings are local • QPE depends on advancing observing systems, while HWRF is a key for hurricanes

  15. Performance measures for “service” programs (e.g., LFW) Performance measures for ST&I (an enabling program) Linking Science, Technology & Infusion Performance Measures to NOAA GPRA Measures Today’s predictive services exist on a foundation of earlier innovation in science and technology Service GPRA (e.g., QPF) Demonstration GPRAs in Testbeds “Infusion” Performance Measures “Science” Performance Measures “Technology” Performance Measures

More Related